import os
import torch

# 主路径
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
print(BASE_DIR)
# SRC_PATH = os.path.join(MAIN_PATH, 'src')
# BASE_DIR = r"F:\DataCenter\study\知识图谱\named-entity-recognition-study"

MAX_VOCAB_SIZE = 10000
START_TAG = "SOS"
STOP_TAG = "EOS"
EMBEDDING_DIM = 5
HIDDEN_DIM = 4
SAVED_MODEL_PATH = os.path.join(BASE_DIR, 'saved_models')
PROCESSED_DATA_PATH = os.path.join(BASE_DIR, 'processed_data')


# 模型设置
MODEL_CONF = {
    'model_name': 'cb_model',
    'save_dir': SAVED_MODEL_PATH,
    # 'attn_model': 'general',
    # 'hidden_size': 500,
    # 'encoder_num_layers': 2,
    # 'decoder_num_layers': 2,
    # 'dropout': 0.5,
    'batch_size': 32,
    # 'bidirectional': False,
    # 'attn_method': 'general',
    # 'save_model_path': None,
    # 'pointer': True,
    # 'is_coverage': False,
    # 'coverage_loss_weight': 0.01,
    # 'weight_tying': False,
}

# GPU还是CPU
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')